Factors Associated With Medication Use Among Individuals Living With Multiple Sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system causing neurological deterioration over time. The objective of this study was to examine the predictors associated with MS medication use. The categories that were investigated were various alternative treatments such as complementary/alternative medications (CAMs), rehabilitation therapy and psychotherapy services as well as comorbid health conditions. The Survey on Living with Neurological Conditions in Canada (SLNCC) 2011-2012 was used (N = 73 347) to carry out a logistic regression model. Individuals who did not take CAMs were more (OR = 5.44, 95% CI 1.37-9.29) likely to use medications for MS. Having a mood disorder was associated with greater use of MS medications (OR = 5.39, 95% CI 1.60-18.17) while back problems were associated with lower odds of medication use (OR = 0.38, 95% CI 0.15-0.98). These factors need to be taken into consideration when creating effective medication adherence interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".